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The aim of this article was to investigate the potential of reverse engineering for developing digital 3D models based on real, worn mechanical components, particularly in cases where original manufacturer documentation is unavailable. The main research question concerned the effectiveness and accuracy of reproducing the geometry of a physical object using various 3D scanning technologies and CAD design tools. The subject of the study was a hydraulic cylinder ball – a worn, deformed component that could no longer be sourced on the market. Two types of 3D scanners were used in the study: a handheld device and a stationary system. Measurement data obtained from each scanner was processed in the VXelements software and then used to create precise models in the SolidWorks environment. Conventional measurements were also conducted and served as a reference point. Comparative analysis showed a high degree of consistency between the methods, with dimensional differences not exceeding 0.4 mm and 2°. The results confirm that reverse engineering, supported by 3D scanning and CAD design, is an effective solution for the individual reconstruction of technical components. The significance of the findings directly relates to industrial practice – particularly in the context of rapid prototyping, spare part reproduction, and maintenance support. The developed methodology can support the digitization and automation of engineering processes, particularly in the context of documentation management and preparation of manufacturing data. In this study, the geometry of a worn component was reproduced without attempting to reconstruct its original shape. The resulting CAD model reflects the current state of wear.
Słowa kluczowe
Wydawca
Czasopismo
Rocznik
Tom
Strony
539--545
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
autor
- Silesian University of Technology Faculty of Materials Engineering ul. Krasińskiego 8, 40-019 Katowice, Poland
Bibliografia
- 1. Pawłowicz, J.A., Knyziak, P., Krentowski, J.R., Mackiewicz, M., Skotnicka-Siepsiak, A. & Serrat, C. (2024). Reverse engineering as a non-invasive examining method of the water tower brick structure condition. Engineering Failure Analysis, 161, 108280. https://doi.org/10.1016/j.engfailanal.2024.108280
- 2. Regassa Hunde, B., Debebe Woldeyohannes, A. (2022). Future prospects of computer-aided design (CAD) – A review from the perspective of artificial intelligence (AI), extended reality, and 3D printing’, Results in Engineering, 14, 100478. https://doi.org/10.1016/j.rineng.2022.100478
- 3. Bacciaglia, A., Ceruti, A., Liverani, A. (2020). Photogrammetry and additive manufacturing based methodology for decentralized spare part production in automotive industry, Advances in Intelligent Systems and Computing. https://doi.org/10.1007/978-3-030-39512-4_121
- 4. Bernaczek, J., Budzik, G., Dziubek, T., Przeszłowski, Ł., Wójciak, K. (2023). Dimensional-shape verification of a selected part of machines manufactured by additive techniques, Advances in Science and Technology Research Journal, 17(1). https://doi.org/10.12913/22998624/157289
- 5. Jović, G., Ćirić, D., Pešić, F., Ivanović, M., Mijajlović, M. (2023). Deviation of the 3D solid model from the printed model’, in: 2023 22nd International Symposium INFOTEHJAHORINA (INFOTEH 2023). https://doi.org/10.1109/INFOTEH57020.2023.10094160
- 6. Klimecka-Tatar, D. and Krynke, M. (2025). Reverse engineering tools – 3D scanning – as support for precise quality control in automated special processes’, Procedia Computer Science, 253, pp. 1933–1942. https://doi.org/10.1016/j.procs.2025.01.255
- 7. Montalti, A., Ferretti, P., Santi, G.M. (2024). A cost-effective approach for quality control in PLA-based material extrusion 3D printing using 3D scanning, Journal of Industrial Information Integration, 41, 100660. https://doi.org/10.1016/j.jii.2024.100660
- 8. Helle, R.H., Lemu, H.G. (2021). A case study on use of 3D scanning for reverse engineering and quality control, Materials Today: Proceedings, 45, pp. 5255–5262. https://doi.org/10.1016/j.matpr.2021.01.828
- 9. Wakjira, Y., Kurukkal, N.S., & Lemu, H.G. (2024). Reverse engineering in medical application: Literature review, proof of concept and future perspectives. Scientific Reports, 14, Article 23621. https://doi.org/10.1038/s41598-024-74176-z
- 10. Subeshan, B., Abdulaziz, A., Khan, Z., & Uddin, M.N. (2022). Reverse engineering of aerospace components utilizing additive manufacturing technology. In TMS 2022 151st Annual Meeting & Exhibition Supplemental Proceedings (pp. 238–246). Springer. https://doi.org/10.1007/978-3-030-92381-5_21
- 11. Dalpadulo, E., Petruccioli, A., Gherardini, F., & Leali, F. (2022). A review of automotive spare-part reconstruction based on additive manufacturing. Journal of Manufacturing and Materials Processing, 6(6), 133 https://doi.org/10.3390/jmmp6060133
- 12. Fabian, M., Huňady, R., & Kupec, F. (2022). Reverse engineering and rapid prototyping in the process of developing prototypes of automotive parts. Manufacturing Technology, 22(2). https://doi.org/10.21062/mft.2022.084
- 13. Helle, R.H., & Lemu, H.G. (2021). A case study on use of 3D scanning for reverse engineering and quality control. Materials Today: Proceedings, 45(6), pp. 5255-5262. https://doi.org/10.1016/j.matpr.2021.01.828
- 14. Zhao, K., Su, Z., Ye, Z., Cao, W., Pang, J., Wang, X., Wang, Z., Xu, X. and Zhu, J. (2023). Review of the types, formation mechanisms, effects, and elimination methods of binder jetting 3D-printing defects. Journal of Materials Research and Technology, 27, pp. 5449-5469. https://doi.org/10.1016/j.jmrt.2023.11.045
- 15. Deng, H., Huang, Y., Wu, S., & Yang, Y. (2022). Binder Jetting Additive Manufacturing: Three-Dimensional Simulation of Micro-Meter Droplet Impact and Penetration into Powder Bed. Journal of Manufacturing Processes, 74, pp. 365-373. https://doi.org/10.1016/j.jmapro.2021.12.019
- 16. Wheat, E., Simch, A., et al. (2023). A Review on Metal Binder Jetting 3D Printing: Process, Materials, and Methods [ICMPC 2023]. E3S Web of Conferences, 430, 01146. https://doi.org/10.1051/e3sconf/202343001146
- 17. Wajdi, F., Tontowi, A.E. (2024). 3D printed stent from graphene-polyethylene glycol diacrylate using digital light processing technique, Management Systems in Production Engineering, 32(4), pp. 555-562. https://doi.org/10.2478/mspe-2024-0053
- 18. Zeiser, A., van Stein, B., & Bäck, T. (2022). Deep learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes. https://doi.org/10.48550/arXiv.2209.10178
- 19. Saimon, A.I., Yangue, E., Yue, X., Kong, Z.J., & Liu, C. (2024). Advancing additive manufacturing through deep learning: A comprehensive review of current progress and future challenges. arXiv. https://doi.org/10.48550/arXiv.2403.00669
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-82d6527a-0dc8-4767-b8e7-1adf5af5a70e
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